Centrality and Universality in Scale-Free Networks (2412.10406v1)

Abstract: We propose a novel paradigm for modeling real-world scale-free networks, where the integration of new nodes is driven by the combined attractiveness of degree and betweenness centralities, the competition of which (expressed by a parameter $0\le p\le 1$) shapes the structure of the evolving network. We reveal the ability to seamlessly explore a vast landscape of scale-free networks, unlocking an entirely new class of complex networks that we call \textit{stars-with-filament} structure. Remarkably, the average degree $\bar k$ of these networks grows like $\log t$ to some power, where $t$ is time and the average shortest path length grows logarithmically with the system size for intermediate $p$ values, offering fresh insights into the structural dynamics of scale-free systems. Our approach is backed by a robust mean-field theory, which nicely captures the dynamics of $\bar{k}$. We further unveil a rich, $p$-dependent phase diagram, encompassing 47 real-world scale-free networks, shedding light on previously hidden patterns. This work opens exciting new avenues for understanding the universal properties of complex networks.

• The paper introduces a novel model that integrates degree and betweenness centrality to redefine preferential attachment in network growth.
• It demonstrates through simulations that varying the attachment parameter p yields distinct phase transitions and power-law growth dynamics.
• The research offers practical insights for designing resilient network architectures and paves the way for future studies in multilayer network dynamics.

Centrality and Universality in Scale-Free Networks

The paper "Centrality and Universality in Scale-Free Networks" introduces a novel framework for modeling real-world scale-free networks by integrating the concept of preferential attachment driven by both degree and betweenness centralities. This paper departs from traditional models by proposing a parameterized approach that simultaneously considers the attractiveness of nodes based on these two centralities, thus creating a broader class of complex networks known as stars-with-filament structures.

Model Overview

The authors develop a dynamic network model using a parameter $0 \leq p \leq 1$, where $p$ determines the likelihood of preferential attachment based on degree centrality versus betweenness centrality. In this model, a newly added node connects to an existing node based on its degree centrality with probability $p$ and based on its betweenness centrality with probability $1-p$. The resulting networks exhibit scale-free properties, characterized by distinct power-law distributions for node degree and betweenness centrality.

Numerical Results and Observations

The paper provides robust numerical simulations to validate the proposed model. One of the key observations is the power-law growth of average degree $\bar{k}$ with respect to time $t$ as $\log(t)$ for intermediate values of $p$, highlighting a non-traditional growth dynamic as opposed to linear scaling. The authors present a rich phase diagram resulting from varying $p$, which captures 47 real-world networks, showcasing a diversity of structures and suggesting a shift in universality classes from the Barabási-Albert model.

In particular, the authors identify a transition in network topology from a star-like graph with many short paths at $p = 0$ to a more distributed, hub-centric structure reminiscent of the Barabási-Albert model at $p = 1$. This transition encompasses networks exhibiting a mixture of super-hubs and branches—a feature coined as stars-with-filament structure.

Theoretical Implications

The theoretical implications of this work are significant in understanding the structural basis of real-world networks. The introduction of betweenness centrality into the growth dynamics of scale-free networks allows for a more nuanced capture of real-world network behaviors, which often deviate from predictions of traditional models. The authors propose scaling relationships and provide mean-field approximations to further shed light on the dynamics and universality of these networks.

Practical Implications and Future Research

Practically, this research has implications for designing more resilient network architectures, particularly in areas where connectivity and information dispersion are critical. The model predicts varying degrees of robustness to random and targeted attacks based on $p$, suggesting potential strategies for optimizing network resilience.

Future developments in this line of research could involve exploring additional centrality measures or extension to multilayer networks, where different types of interactions play a pivotal role. Examining the dynamic processes on these networks, such as information spreading and epidemic modeling, presents another avenue for valuable exploration.

Overall, this paper provides a comprehensive framework and theoretical basis to enrich the understanding and modeling of complex networks by addressing both their structural nuances and dynamic growth patterns.